Evaluating the impact of NPC1 SNPs on entry efficiency of Filovirus in vitro: agent-based model approach

The interaction between the Niemann-Pick C1 (NPC1) protein and the glycoprotein (GP) of filoviruses is essential for viral entry into host cells. Single nucleotide polymorphisms (SNPs) in NPC1, which lead to amino acid substitutions, can significantly alter viral entry efficiency. However, the quantitative impact of these SNPs remains unclear. To address this, we investigate in vitro cell-to-cell infection using a plaque assay with vesicular stomatitis virus (VSV) expressing Ebola and Marburg GP. We employe an agent-based model (ABM) to estimate entry efficiency by simulating plaque growth, enabling a comparative analysis of SNP-specific effects on viral entry. Our quantitative analysis reveals that D508N, P424A, and S425L substitutions in NPC1 reduce entry efficiency in both viruses. These findings provide insights into host-pathogen interactions and demonstrate the value of ABM in virology research, potentially guiding therapeutic strategies targeting viral entry mechanisms. Author summaryEbola and Marburg viruses are highly pathogenic filoviruses that cause severe hemorrhagic fever in humans, with case fatality rates reaching around 50%. These viruses pose significant public health challenges due to their potential for large-scale outbreaks. A key step in their infection process is the interaction between the Niemann-Pick C1 (NPC1) protein on host cells and the viral glycoprotein, which facilitates viral entry. Genetic variations in NPC1 caused by single nucleotide polymorphisms (SNPs) can lead to amino acid substitutions, potentially altering the efficiency of viral entry. To better understand this process, we developed an agent-based model, a computational approach that simulates individual cell interactions and provides spatial resolution not achievable with traditional modeling techniques, to simulate viral plaque growth in vitro. By applying this model, we quantified how specific NPC1 substitutions, such as D508N, P424A, and S425L, affect the entry efficiency of Ebola and Marburg viruses. Our findings highlight the potential of computational modeling to uncover the impact of genetic variations on viral infections and provide insights that may inform therapeutic strategies against these deadly viruses.